dataset_type = 'CocoDataset'
data_root = './data/visdrone/ExDark/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile', to_float32=True),
    dict(type='LoadAnnotations', with_bbox=True),
    # dict(
    #     type='Expand',
    #     mean=img_norm_cfg['mean'],
    #     to_rgb=img_norm_cfg['to_rgb'],
    #     ratio_range=(1, 2)),
    # dict(
    #     type='MinIoURandomCrop',
    #     min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
    #     min_crop_size=0.3),
    dict(type='Resize', img_scale=[(320, 320), (416, 416)], keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(416, 416),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
# data = dict(
#     samples_per_gpu=8,
#     workers_per_gpu=4,
#     train=dict(
#         type=dataset_type,
#         ann_file=data_root + 'main/train.txt',
#         img_prefix=data_root + 'JPEGImages/IMGS',
#         pipeline=train_pipeline),
#     val=dict(
#         type=dataset_type,
#         ann_file = data_root + 'main/val.txt',
#         img_prefix=data_root + 'JPEGImages/IMGS',
#         pipeline=test_pipeline),
#     test=dict(
#         type=dataset_type,
#         ann_file = data_root + 'main/val.txt',
#         img_prefix=data_root + 'JPEGImages/IMGS',
#         pipeline=test_pipeline))
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,                                                                             
        ann_file=data_root + 'train.json',
        # img_prefix=data_root + 'NaFNet_Out/',
        img_prefix=data_root + 'out/images/train/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'val.json',
        img_prefix=data_root + 'out/images/val/',
        pipeline=test_pipeline),    
    test=dict(
        # samples_per_gpu=16,
        type=dataset_type,
        ann_file=data_root + 'val.json',
        img_prefix=data_root + 'out/images/val/',
        pipeline=test_pipeline))